The purpose of this small grant proposal is to develop methodological and statistical procedures to determine mediation, or how prevention programs change outcome variables. Mediation is assessed by linking program effects on intervening variables with effects on outcomes. The need for these procedures became apparent in a NIDA funded evaluation of a community based drug prevention program in two midwestern cities. First, when dependent variables are measured on an ordinal scale, and appropriate models are applied (logistic or probit regression), rules of mediation applicable in the case of continuous dependent variables do not apply. In this situation, it is possible that substantial mediation exists but evaluating mediation as if the variables were continuous leads to the conclusion of no mediation or even suppression of program effects. A related problem occurs in the analysis of the majority of prevention studies where the program variable is binary (treatment versus control) not continuous. Measures of the extent of mediation such as the proportion of the program effect mediated and the ratio of mediated to non-mediated effect require development. Finally, while mediation models in cross-sectional studies are straightforward, several alternative models are plausible for longitudinal data and different models can yield different conclusions about the magnitude of mediation. The proposed work entails simulation and analytical studies of measures of mediation for categorical and continuous dependent, independent, and mediator variables, in designs that differ in the number of follow-up measurements, sample size, extent of mediation, and measurement error. The mediation methods developed will be applicable to all prevention studies.